Defining the diverse spectrum of inversions, complex structural variation, and chromothripsis in the morbid human genome

R E S E A R C H

Open Access

Defining the diverse spectrum of

inversions, complex structural variation,

and chromothripsis in the morbid

human genome

Ryan L. Collins

, Harrison Brand

, Claire E. Redin

, Carrie Hanscom

, Caroline Antolik

, Matthew R. Stone

,

Joseph T. Glessner

, Tamara Mason

, Giulia Pregno

, Naghmeh Dorrani

, Giorgia Mandrile

, Daniela Giachino

,

Danielle Perrin

, Cole Walsh

, Michelle Cipicchio

, Maura Costello

, Alexei Stortchevoi

, Joon-Yong An

,

Benjamin B. Currall

, Catarina M. Seabra

, Ashok Ragavendran

, Lauren Margolin

, Julian A. Martinez-Agosto

,

Diane Lucente

, Brynn Levy

, Stephan J. Sanders

, Ronald J. Wapner

, Fabiola Quintero-Rivera

,

Wigard Kloosterman

and Michael E. Talkowski

Abstract

Background: Structural variation (SV) influences genome organization and contributes to human disease. However, the complete mutational spectrum of SV has not been routinely captured in disease association studies.

Results: We sequenced 689 participants with autism spectrum disorder (ASD) and other developmental abnormalities to construct a genome-wide map of large SV. Using long-insert jumping libraries at 105X mean physical coverage and linked-read whole-genome sequencing from 10X Genomics, we document seven major SV classes at ~5 kb SV resolution. Our results encompass 11,735 distinct large SV sites, 38.1% of which are novel and 16.8% of which are balanced or complex. We characterize 16 recurrent subclasses of complex SV (cxSV), revealing that: (1) cxSV are larger and rarer than canonical SV; (2) each genome harbors 14 large cxSV on average; (3) 84.4% of large cxSVs involve inversion; and (4) most large cxSV (93.8%) have not been delineated in previous studies. Rare SVs are more likely to disrupt coding and regulatory non-coding loci, particularly when truncating constrained and disease-associated genes. We also identify multiple cases of catastrophic chromosomal rearrangements known as chromoanagenesis, including somatic chromoanasynthesis, and extreme balanced germline chromothripsis events involving up to 65 breakpoints and 60.6 Mb across four chromosomes, further defining rare categories of extreme cxSV. Conclusions: These data provide a foundational map of large SV in the morbid human genome and demonstrate a previously underappreciated abundance and diversity of cxSV that should be considered in genomic studies of human disease.

Keywords: Structural variation, Inversion, Complex chromosomal rearrangement, Chromoanagenesis, Chromothripsis, Autism, Neurodevelopmental disorders, Copynumber variation, Whole-genome sequencing, Germline mutation

* Correspondence:talkowski@chgr.mgh.harvard.edu

1_{Molecular Neurogenetics Unit and Psychiatric and Neurodevelopmental}

Genetics Unit, Center for Genomic Medicine, and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA

2_{Program in Bioinformatics and Integrative Genomics, Division of Medical}

Sciences, Harvard Medical School, Boston, MA 02115, USA Full list of author information is available at the end of the article

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Background

Structural variation (SV), or the rearrangement of chromosomal segments (≥50 bp), is a major driver of the organization and content of individual genomes [1]. SV manifests in multiple mutational forms, canonically cate-gorized as“balanced” SV—rearrangements lacking major gain or loss of genomic DNA, such as inversions, multiple classes of insertions, and translocations—and “unbalanced” SV, or copy number variants (CNV), which involve changes in DNA dosage [2, 3]. Recent research has demonstrated that some rearrangements have multiple, compounded mutational signatures and do not fit into a single canonical SV category [4–9]. These non-canonical, complex SVs (cxSV) span a heterogeneous range from relatively simple CNV-flanked inversions to extreme rearrangements involv-ing dozens of loci across multiple chromosomes [4, 10]. The most severe cxSVs are thought to involve sudden chromosome pulverization and reorganization; this group of ultra-rare, catastrophic cxSVs are known collectively as chromoanagenesis [11], which encompasses three core pro-posed mechanisms: chromothripsis [12]; chromoanasynth-esis [13]; and chromoplexy [14]. The most commonly reported of these, chromothripsis, was first observed in cancer with interspersed deletion bridges between frag-ments of derivative chromosomes [12, 15, 16], while subse-quent studies discovered both balanced and unbalanced forms of chromothripsis in the human germline [9, 10, 17, 18]. Though less frequently reported, chromoanasynthesis and chromoplexy have also been observed in the human germline [9, 13, 19–23]. Despite these discoveries, the pat-terns, rates, and properties of cxSVs have primarily been the focus of cancer genomics and such rearrangements remain largely underappreciated in the human germline.

Recent studies have begun to profile SV at sequence resolution in healthy human populations, such as the 1000 Genomes Project and the Genome of the Netherlands Consortium [1, 24], though most population-scale stud-ies to date have not deeply characterized balanced SVs or cxSVs. Indeed, while somatic cxSV has been an emphasis in analyses of tumor genomes [25–27], inves-tigations of SV in germline disease have predominantly been restricted to gross chromosomal abnormalities and large, de novo CNVs [9, 28–36]. Several studies of germline SV have demonstrated that a subset of SV represents an important class of penetrant, pathogenic loss-of-function (LoF) mutations that are not broadly ascertained in human disease studies [4, 5, 37–39]. By example, imputed genotypes of polymorphic SVs at the major histocompatibility complex (MHC) and hapto-globin (HP) loci in large populations have demon-strated disease relevance for schizophrenia and untoward cardiovascular lipid phenotypes, respectively [40, 41]. To date, no population-scale disease studies have evaluated the full mutational spectrum of large

SV—specifically including balanced SV and cxSV—though there is a pressing need for such SV maps with the upcoming emergence of large-scale whole-genome se-quencing (WGS) studies to characterize the genetic archi-tecture of human disease.

Here, we performed long-insert whole-genome se-quencing (liWGS) on 689 participants diagnosed with autism spectrum disorder (ASD) or other developmental disorders to benchmark the population-level landscape of complex and large SVs in a relevant disease cohort. liWGS is optimized to provide deep physical coverage (mean 105X) by large fragments (mean 3.5 kb) capable of detecting large SVs, including some variants that may be intractable to standard short-insert WGS (siWGS) due to repetitive sequences and microhomology that often mediate SV breakpoints, with the primary limita-tion being its comparatively limited effective resolulimita-tion (~5 kb) [42, 43]. These data yielded a catalog of seven major SV classes and further revealed 16 recurrent sub-classes of cxSV, most of which had not been classified in human disease studies. Further analyses identified a sur-prising abundance and diversity of inversion variation and derived a broad spectrum of rare cxSV in every gen-ome surveyed, which collectively displayed many of the hallmarks of deleterious biological significance and evo-lutionary selection. This study also detected three cases of extreme germline chromoanagenesis, which were in-tegrated into an analysis of all previously reported cases of chromoanagenesis in the literature to define the prop-erties of germline chromoanagenesis. These data pro-vided an initial atlas of SV in the morbid germline that can be used as a benchmarking resource for future in-vestigations and suggest that balanced SV and cxSV are relatively common in the human genome, warranting consideration in genetic studies of disease.

Results

Sample selection and genome sequencing

We selected 686 participants diagnosed with idiopathic ASD from the Simons Simplex Collection (SSC) [44]. All participants from the SSC met standardized diagnostic criteria for ASD and many included co-morbid diagno-ses of intellectual disability, developmental delay, or seizures. All participants had two unaffected parents and at least one unaffected sibling available from the SSC. Independently, we recruited three unrelated participants presenting with neurodevelopmental disorders (NDD) or congenital anomalies and a de novo translocational in-sertion ascertained by clinical karyotyping that appeared to harbor additional complexity. We performed liWGS on all 689 participants to a mean insert size of 3.5 kb and a mean physical coverage of 105X as shown in Fig. 1a and b [42, 43].

Discovery and validation of a diverse spectrum of SV in the morbid human genome

Among the initial 686 SSC participants, analyses re-vealed a highly heterogeneous landscape of 11,735 dis-tinct SVs at the resolution of liWGS, representing a total of 436,741 SV observations or a mean of 637 large SVs per genome (Additional file 1 and Fig. 1c and d). Exten-sive validation was performed to evaluate the SV detec-tion methods used: one-third of all fully resolved SVs (33.8%; 3756/11,108) were assessed using a combination of five orthogonal approaches, as detailed in Additional file 2: Supplemental Results 1 and Supplemental Table 1. These experiments estimated a global false discovery rate (FDR) of 10.6% and false negative rate (FNR) of 5.9% for SV discovery from liWGS. Performance was best for cxSVs (2.6% FDR; see Additional file 2: Supple-mental Note 1) and canonical deletions (5.3% FDR), which collectively comprised the majority (57.4%) of all SVs. As anticipated, validation rates were lowest for insertions (22.9% FDR), the majority of which are known to be smaller than the resolution of liWGS (e.g. SVA and Alu mobile element insertions) [1, 7, 45] and represent a major challenge for liWGS detection. Excluding this category of variation, the overall FDR improved to 9.1%. Importantly,

16.8% (1968/11,735) of all SVs were either balanced or complex, emphasizing that an appreciable fraction of large SV per genome is overlooked when restricting analyses to canonical CNVs alone. These analyses also found that 10.9% (75/686) of all participants harbored at least one very large, rare SV (≥1 Mb; variant frequency (VF) < 1%), impli-cating rare SV as a frequent source of large structural diver-gence between individual genomes (Fig. 1e and f).

Novel SV sites and rearrangement complexity

This SV map was compared with six recent WGS SV studies outside of the SSC [1, 5, 7, 46–48], the Database of Genomic Variants (DGV) [49], and the InvFEST inversion database [50], which determined that 38.1% (4233/11,108) of all SVs detected in this study (excluding incompletely resolved sites, n = 627/11,735) had not been previously re-ported. This was particularly true for cxSVs, nearly all which were novel to this study (93.8%; 271/289), in-cluding 50.2% for which at least one breakpoint had been observed previously but likely misclassified as ca-nonical SVs (e.g. Additional file 2: Figure S1). Notably, 97.4% of cxSVs were validated in the present study; however, due to the limited resolution of liWGS we predict that this is likely to be an underestimate of the

Fig. 1 The diverse landscape of SV in participants with ASD and other developmental disorders. We sequenced the genomes of 689 participants with ASD and other developmental disorders. a Physical coverage and (b) median insert size of liWGS libraries. c Count and distributions of large SV detected by liWGS (Additional file 1). d Distribution of SVs per participant by SV class. e Density plots of SV sizes by class. Characteristic Alu and L1 peaks are absent due to the resolution of liWGS (> ~ 5 kb) being larger than most mobile element insertions. f Cumulative distributions of SV frequencies by class. Singletons (single observation among all 686 samples) are marked with an arrow. Rare SVs are defined as those with variant frequency (VF) < 1%

complexity associated with these variants and their overall structure as liWGS is blind to micro-complexity at SV breakpoints, and the resolution to delineate components of cxSVs comprised of small variants (< 5 kb) is limited (Additional file 2: Supplemental Note 1) [1, 10, 51, 52]. In sum, these data revealed that large cxSVs in humans are substantially more abundant and diverse than has been previously appreciated.

Defining and contrasting 16 distinct subclasses of large, recurrent cxSV

The frequency of novel, large cxSVs in this cohort led us to further characterize their mutational spectra. We ob-served that 42.6% (123/289) of all cxSVs were poly-morphic (i.e., appearing in at least two participants), and each participant harbored a median of 14 large cxSVs (range: 6–23 cxSVs per genome), establishing that cxSV is a standing class of variation present in most, if not all,

human genomes. We classified 16 unique subclasses of re-current and relatively common cxSVs for consideration in future genomic studies, as presented in Fig. 2. Each cxSV subclass appeared in at least five participants and featured a signature variant allele structure. The majority of these subclasses (10/16) were unbalanced inversions and thus most cxSVs (84.8%) involved at least one inverted seg-ment. Correspondingly, CNV-flanked inversions com-prised the largest group of cxSVs (77.2%), with complex duplications being larger and rarer on average than complex deletions (Additional file 2: Figure S2). Both deletions and duplications flanking complex inversions were equally likely to arise at either inversion break-point, consistent with either replicative repair-based mechanisms such as MMBIR/FoSTeS [6, 39, 53] or syn-chronous repair of multiple simultaneous double-strand breaks [18, 54]. Most cxSVs were intrachromo-somal, with relatively few rearrangements (3.1%; 9/289)

Fig. 2 Classifying 16 recurrent subclasses of large, complex SVs in the human genome. At liWGS resolution, we identified 16 recurrent classes of cxSV, defined here as non-canonical rearrangements involving two or more distinct SV signatures or at least three linked breakpoints. We validated 97.4% (150/154) of all cxSV sites assessed by at least one assay. Each participant harbored a median of 14 cxSVs at liWGS resolution (range: 6–23 cxSVs per participant). We identified 289 distinct cxSVs across 686 participants, totaling 9666 cxSV observations. Each row represents a subclass of cxSV, with columns representing the subclass abbreviation, number of distinct variants discovered, validation rate, total number of observed variants across all participants, the percentage of participants that were found to harbor at least one such variant in their genome, the median size of all variants in that subclass, each subcomponent SV signature that comprises the class, a linear schematic of each class of cxSV, and a simulated example of the copy-number profile as would be observed by chromosomal microarray or WGS

involving two or more chromosomes. As discussed above, these 16 cxSV subclasses certainly represent a conservative initial catalog of the full complement of cxSV in humans given the resolution of liWGS.

Abundance of canonical and complex inversion variation

Routine detection of large inversion variation has his-torically been a challenge for high-throughput tech-nologies, including siWGS [1, 50, 55–57]. Although recent advances in long-read and strand-specific WGS represent promising novel platforms for inversion dis-covery [7, 58, 59], liWGS remains particularly well suited for inversion detection as the distance spanned between paired reads (~3.5 kb) avoids most confound-ing repetitive sequences and imbalances that frequently occur at inversion breakpoints [6, 10]. In this cohort, liWGS identified a median of 87 inversion variants per participant, a surprising fraction of which (12.6%; 11/ 87) were complex (Additional file 2: Figure S3A). These complex inversions were larger on average than canon-ical inversions (Additional file 2: Figure S3B) and were also significantly enriched in rare variants (VF < 1%): 75.9% of complex inversions were rare (186 rare/245 total), while only 43% of canonical inversions were rare (169 rare/393 total) (p = 1.2 × 10–16), which suggests that complex inversions might be under relatively in-creased purifying selection. It is possible that this trend may also be attributable in part to a correlation be-tween SV frequency and average size [1], as larger in-versions might be less viable in the germline either due to increased deleterious consequences or by obstructing recombination [60]. The number of inversions per gen-ome identified in this study was approximately twofold greater than estimates from the 1000 Genomes Project from low-depth siWGS on 2504 samples [1]. Given the validation rate for inversions (canonical inversion: 89.8%; complex inversion: 96.9%), we hypothesized that this difference may be due to inversion breakpoints be-ing enriched near longer repetitive sequences, which might confound siWGS but would still be accessible to liWGS. Indeed, we found that 87.6% of all inversion-associated variants (both complex and canonical; n = 636) had one or both breakpoints within ±500 bp (i.e. conservative liWGS breakpoint resolution) of a rela-tively long (≥300bp) annotated repetitive sequence [61], and both breakpoints were in proximity to long re-petitive sequence for 54.9% of inversions. Both observa-tions significantly deviated from the null distribution from 1 million matched simulations (p < 1.0 × 10–6), as shown in Additional file 2: Figure S3C. This included inversion breakpoints in segmental duplications, des-pite the limited power of short-read sequencing to de-tect variation at these loci, consistent with previously proposed mechanistic hypotheses of inversion formation

[58, 59, 62]. Collectively, the patterns of canonical and complex inversions observed herein suggest that a sub-stantial fraction of such variation may be preferentially ac-cessible to sequencing technologies like liWGS that provide long-range information on genome structure.

Resolving intractable rare cxSV with linked-read WGS

We performed linked-read WGS (lrWGS) from 10X Gen-omics [63] to resolve large, rare cxSVs detected by liWGS in three participants for which the liWGS delineated rear-rangements that were not fully resolved by orthogonal validation. We sequenced these three participants and two parents to a median of 31.1X nucleotide coverage. From these data, we resolved all breakpoints of each predicted large cxSV, notably including a de novo complex translocation in a participant with ASD that involved 550 kb of inverted sequence and three breakpoints pre-dicted by liWGS, two of which could not be validated by traditional approaches (polymerase chain reaction (PCR) and Sanger) or by siWGS due to low sequence uniqueness flanking the junctions (Fig. 3). All three breakpoints were confirmed and phased by 104 inde-pendent lrWGS molecules, revealing disruption of the genes PARK2 and CAMKMT. The other two large cxSVs validated by lrWGS are provided in Additional file 2: Figures S4 and S5. Building upon our earlier observations of inversion variation, these data fur-ther suggest that technologies that provide long-range structural information will be of value for resolving large complex chromosomal abnormalities, and com-prehensive analyses are required in larger samples to determine the improved yield of SVs from lrWGS as compared to siWGS, liWGS, or other emerging technologies.

Rare SVs exhibit multiple hallmarks of deleterious biological consequences

Consistent with trends observed among rare coding point mutations [64–67], rare SVs (VF < 1%) appeared to be considerably more deleterious than common poly-morphic SVs (VF > 1%) based on computational annota-tions (Additional file 2: Supplemental Results 2). Rare SVs in this cohort were larger than common SV, in line with observations from the 1000 Genomes Project [1], and were also nearly twice as likely to disrupt multiple classes of regulatory non-coding elements, and 1.5-fold more likely to result in predicted LoF of genes (all com-parisons were significant and test statistics are provided in Fig. 4a and b and Additional file 2: Table S2). The set of genes truncated by rare LoF SVs in this study was also approximately twofold enriched in disease-associated genes [68–70], genes intolerant to functional mutation [65–67], and genes with burdens of exonic deletions in NDDs [38] (Fig. 4c and Additional file 2: Table S3.) These

Fig. 3 liWGS and lrWGS resolved a de novo gene-disrupting cxSV that was cryptic to standard siWGS. We performed lrWGS from 10X Genomics (Pleasanton, CA, USA) as a method of orthogonal validation for three large complex SVs detected by liWGS, two of which failed to fully validate by traditional methods. One notable example is shown here; the other two are provided in Additional file 2: Figures S4 and S5. a A de novo complex reciprocal translocation with three breakpoints between chromosomes 2 (pink) and 6 (green) was discovered by liWGS in a participant with ASD and predicted to result in LoF of PARK2 and CAMKMT. However, two of three breakpoints (breakpoints #1 and #3; orange) were not detectable by siWGS. b lrWGS heatmaps from Loupe software [113] analysis of lrWGS data showed clear evidence for each of the three SV breakpoints. c lrWGS resolved and phased all three breakpoints, including both breakpoints that failed molecular validation due to low-complexity repetitive sequence (blue), which were resolved by spanning the low-complexity sequence with 28 liWGS reads and 30 lrWGS molecules at breakpoint #1 and 12 liWGS reads and 41 lrWGS molecules at breakpoint #3

findings were concordant with the hypothesis that loci sensitive to disruptive point mutations in healthy individ-uals would also show selective pressure against deleterious SV. Finally, we identified ten specific loci that were signifi-cantly enriched for rare SVs beyond genome-wide

expectations (Additional file 2: Supplemental Results 3, Figure S6 and Tables S4–5), five of which involved genes with evidence for roles in a broad spectrum of neuro-logical disorders (PARK2, IMMP2L, CTNNA3, CYFIP1, PTPRT) [32, 71–75]. Additional SV studies in larger

Fig. 4 Rare SVs are enriched for hallmarks of deleterious biological outcomes. Comparing all rare (VF < 1%) and common (VF > 1%) SVs discovered in this cohort revealed differences in their respective functional annotations (Additional file 2: Table S2). a Rare SVs were larger on average than common SVs [1]. b Rare SVs were more likely than common SVs to disrupt genes, particularly when the disruption was predicted to result in LoF. Rare SVs were also more likely than common SVs to result in disruption of promoters [112, 114], enhancers [112, 114], and TAD boundaries [110]. c Genes predicted to harbor at least one LoF mutation due to a rare SV were enriched in many subcategories when compared to common SV, including genes predicted to be constrained against truncating mutations in healthy individuals (Constrained) [65, 66], genes predicted to be intolerant of functional variation in healthy individuals (Intolerant) [67], genes with significant burdens of exonic deletions in NDD cases versus healthy controls (NDD ExDels) [38], genes associated with an autosomal dominant disorder (Autosomal Dom.) [68, 69], and genes with at least one pathogenic variant reported in ClinVar (Disease Assoc.) [70] (Additional file 2: Table S3)

matched case-control cohorts will be required to elucidate any role of SV at these loci in disease risk, and such stud-ies are ongoing.

Extreme chromoanagenesis in aberrant human development

The most catastrophic SVs catalogued to date involve the cxSV subclass known as chromoanagenesis. To summarize existing knowledge of chromoanagenesis and contextualize the findings from this study, we conducted a literature review of published reports of germline chro-moanagenesis at sequence resolution, almost all of which arose de novo in affected individuals. The results of this review are consolidated in Table 1 and Additional file 2: Table S6 [9, 10, 13, 17–23, 76–78]. Based on this know-ledge, and separate from the genome-wide SV analysis of the 686 SSC participants described above, we performed liWGS on an additional three unrelated participants (participants TL010, UTR22, and TL009) with develop-mental anomalies and large de novo translocational inser-tions identified by clinical karyotyping, which we suspected may represent more complex rearrangements. The rearrangement in subject UTR22 has since been re-cently described [9]. Sequencing analysis revealed that the first two participants, TL010 and UTR22, harbored ex-treme yet almost entirely balanced germline chromothrip-sis events, each involving > 40 breakpoints, >40 Mb of rearranged sequence, four chromosomes, and LoF of > 12 genes, yet < 1 Mb of total dosage imbalance (Fig. 5a and b, Additional file 2: Table S7, and Additional file 3).

In contrast to the first two participants, TL009 har-bored a somatic mosaic unbalanced chromoanasynthesis

of chromosome 19, involving 19.1 Mb of duplicated DNA, copy gain (CG) of 567 genes, 361.2 kb of de-leted DNA, and LoF of 12 additional genes (Fig. 5c and Additional file 3). Intriguingly, while all eight du-plicated loci arose on the maternal homologue, 6/8 of these duplications were predicted to be mosaic from liWGS (2.57 ± 0.02 copies, 95% confidence interval (CI)), yet the other 2/8 duplications appeared at nearly three full copies (2.93 ± 0.10 and 2.83 ± 0.09 copies, 95% CIs), which may contrast previous assumptions that chromoanasynth-esis arises in a single mutational process. Both of the ap-parently higher-copy-state loci were significantly greater in copy number than the six mosaic duplications (p = 3.60 × 10–12 and p = 9.18 × 10–8) but not different from each other (p = 1.04 × 10–1) (Fig. 5d). Remarkably, these two duplications were connected by a 5.1 Mb interstitial inversion, resulting in a mutational signature that matches the dupINVdup cxSV subclass previously described (Fig. 2) [4]. We speculated that the rearrangement in TL009 may have arisen initially as a de novo dupINVdup either in the maternal germline or very early in embryonic develop-ment, and was subsequently compounded by a second mutational event, possibly through mitotic missegregation driven by genome instability from the large dupINVdup near the centromere (Additional file 2: Figure S7). These three cases further illustrate that extreme chromothripsis can arise in the germline while often resulting in near dosage-neutral derivatives and that unbalanced chromoa-nasynthesis can arise in soma, perhaps in a temporally punctuated series of rearrangements more closely resem-bling the compounded mutations of chromoplexy than a single catastrophic mutational process [14, 79].

Table 1 Characteristics of chromoanagenesis classes

Chromothripsis Chromoanasynthesis Chromoplexy

Mutational event Single Single or multiple Single or multiple

Chromosomes Few (1–4) Few (usually 1) Many (usually≥ 4)

Breakpoints Many (≥5; sometimes > 25) Fewer (usually 5–25) Fewer (usually 5–25)

Breakpoint distribution Clustered Clustered Interspersed (usually in active

chromatin)

Breakpoint signature Blunt ends Microhomology Blunt ends

Dosage alteration Cancer: often unbalanced

(deletion bridges); Germline: mainly balanced (<5% of total rearrangement)

Unbalanced (predominantly copy gain)

Mainly balanced (occasional deletion bridges)

Proposed mechanism Micronucleus missegregation

+ chromosome pulverization + NHEJ

Micronucleus missegregation + chromosome pulverization + MMBIR/FoSTeS

Multiple DSBs during active transcription + NHEJ

Proposed parent-of-origin bias Paternal None None

Proposed transmission bias Maternal None None

Germline reports 43 10 6

Case:Control 39:4 10:0 6:0

Discussion

By applying an approach optimized for genome-wide SV discovery to a cohort of nearly 700 participants with ASD and related developmental disorders, these data provided a glimpse of the diverse mutational landscape of large SVs in the morbid human germline. Analyses revealed a substantial number of novel canonical and complex SV sites, and a wide breadth of large cxSV mu-tational signatures. Ascertaining SVs with liWGS also uncovered a surprising abundance of canonical and complex inversion variation, some of which were likely to be intractable to siWGS due to local sequence charac-teristics in proximity to the breakpoints. Importantly, owing to the limited resolution of liWGS, the barriers to SV detection using short-read sequencing, and the limi-tations of reference-based alignments more broadly [24], the diversity of cxSVs described here still likely accounts for only a fraction of the mutational landscape of cxSV in the human germline, and likely underestimates the se-quence-level complexity of the variants reported herein. We anticipate many additional subclasses will continue to

be discovered from larger population-scale studies and higher resolution technologies. Finally, annotation of the balanced SVs and cxSVs identified in this cohort demon-strated that these classes of variation contributed a modest but meaningful number of perturbations of coding and noncoding regulatory loci per genome, the effects of which were predicted to be particularly deleterious among rare variants, suggesting that routine characterization of the complete spectrum of SV in genetic studies of human dis-ease may improve power to resolve the genetic etiologies of some disorders. In sum, these data thus represent a bench-mark for major classes of large SVs that will be expanded by future efforts.

These analyses indicate that large and complex chromosomal abnormalities are relatively common in the human germline, and that numerous large cxSVs likely exist in every human genome, with the most ex-treme cxSVs (e.g. chromoanagenesis) representing one tail of the distribution of SV complexity and size. While still rare, our data confirm that non-tumorigenic chro-moanagenesis exists as both constitutional and somatic

Fig. 5 Extreme chromoanagenesis manifests by multiple mutational mechanisms in three participants with developmental anomalies. We applied WGS to resolve microscopically visible cxSVs in three unrelated participants with developmental abnormalities. a, b Circos representations of two cases of extreme and largely balanced chromothripsis, involving > 40 breakpoints, > 40 Mb, and > 12 genes across four chromosomes [9, 115]. Points plotted around the inner ring represented estimated copy number alterations; deletions are highlighted in red. Links represent non-reference junctions on derivative chromosomes. c Circos representation of a somatic mosaic chromoanasynthesis event of chromosome 19 [115]. Duplications are shaded in blue and interspersed duplications are designated by shaded ribbons leading from the duplicated sequence to their insertion site. d CMA and WGS analysis of the mosaic chromoanasynthesis from panel c (participant TL009) revealed all nine CNVs involved in the rearrangement to have arisen on the maternal homologue and that 6/8 duplications were apparently mosaic (2.57 ± 0.02 copies, 95% CI; median coverage shown in yellow; yellow shading indicates 95% CI). Surprisingly, 2/8 duplications (outlined in teal) exhibited significantly greater copy numbers than the other six (p = 9.18 × 10–8), were linked by an underlying interstitial inversion and appeared to represent approximately three copies, suggesting this rearrangement might have originated as a de novo dupINVdup cxSV in the maternal germline (Additional file 2: Figure S7)

variation and that cytogenetically detected de novo inter-chromosomal insertions may hallmark such extreme re-arrangements, though larger collections of samples are warranted to further investigate this phenomenon. The review of chromoanagenesis literature performed herein [10, 13, 17–23, 76–78] (Table 1 and Additional file 2: Table S6) supports three conclusions: (1) constitutional chromoanagenesis is frequently balanced, possibly due to embryonic selection against loss of genes intolerant to haploinsufficiency [79–81]; (2) extreme genomic rear-rangements can be tolerated in the developing germline [77, 78], although cases of unbalanced extreme chro-moanagenesis have mostly been reported in cancer; and (3) at least 2/55 of these rearrangements appeared to be the product of multiple compounding mutational events [23] and another 4/55 rearrangements were observed to acquire additional rearrangements de novo upon unstable transmission from parent to child [23, 77], suggesting it is unlikely that such catastrophic rearrangements always arise in a single mutational event. This latter conclusion draws a key parallel between the two prevailing proposed mechanisms of cancer chromoanagenesis, wherein some rearrangements likely arise from DNA shattering in misse-gregated micronuclei during mitosis [12, 54, 82–85], yet others acquire additional breakpoints over punctuated tumor evolution [14, 79, 86], not unlike the six constitu-tional rearrangements with some degree of evidence against a singular mutational event [23, 77].The mosaic chromoanasynthesis characterized in this study may be an exemplar of such mutational progression, as two of the largest duplications appeared to represent germline dupli-cations (copy state ~ 3), whereas the remaining rearrange-ments were present at lower mosaic fractions (copy state ~ 2.5), possibly indicating progressive mutational acquisi-tion. Further study into the mechanisms of such alterations, and comparisons to the micronuclei hypoth-esis, would be of great interest in our evolving under-standing of this phenomenon.

Conclusions

This study provides new insights into the extensive and diverse subclasses of SVs in the morbid human genome and illuminates that inversion variation is substantially more complex than has been appreciated from other technologies. The patterns of variation defined here extend previous maps of SVs in the general population [1, 24], and functional annotations of the SVs in this cohort demonstrate that rare SVs are more likely than common SV to disrupt both coding and regulatory non-coding elements. These analyses further suggest that genes truncated by rare SV are more likely to be con-strained against inactivating point mutations in healthy individuals and associated with disease phenotypes in large clinical databases. The presentation of three cases

of chromoanagenesis further support earlier evidence that extremely complex balanced rearrangements are tolerated in the human germline, and suggest that some catastrophic constitutional rearrangements may arise through multiple mutational events. This study empha-sizes the need for detailed characterizations of SVs to aid in interpretation of the morbid human genome, and these data provide a reference map of inversions and cxSVs to be built upon by population-scale sequencing studies.

Methods

Sample selection and phenotyping

Samples included in genome-wide analyses (n = 686) were acquired from the SSC, a cohort of 2591 simplex autism families, each with one affected child, one or more unaffected siblings, and two unaffected parents collected from 12 sites across the United States [44]. We randomly selected 230 unrelated SSC probands, and se-lected the remaining 456 on the basis of no known pathogenic de novo gene-truncating point mutation or large de novo CNV from prior whole exome sequencing (WES) and CMA analyses [36]. All probands selected from the SSC met standardized diagnostic criteria be-tween the ages of four and 16 years for ASD and often one or more additional neurodevelopmental anomalies, which in this study included developmental delay (60.7%), intellectual disability (31.6%), and seizures (12.3%). Phenotype information for each sample was previously ascertained by the SSC investigators (see “Acknowledgements”) and we obtained these data with permission through the online SFARIbase portal (http:// sfari.org/resources/sfari-base). DNA was obtained through SFARI from the Coriell Cell Repository at Rutgers Univer-sity (Camden, NJ, USA). The three cases with cytogeneti-cally detected de novo translocational insertions were referred by the University of Torino (Italy), the Columbia University Medical Center (USA), and the UCLA Clinical Genomics Center (USA) based on cytogenetic findings from G-banded karyotyping. Informed consent was ob-tained for all patients (either during collection by the SSC or at the referring sites) and all samples (except UTR22) were sequenced with approval from the Partners Health-care Institutional Review Board. Ethical approval for sequence analysis of case UTR22 was given by the ethical committee of the San Luigi Gonzaga University Hospital-Orbassano (TO) Italy.

liWGS library preparation and sequencing

Custom liWGS libraries were constructed using our previously published protocols for all samples except case UTR22, the protocol for which is described below [42, 43]. One library was prepared and sequenced per par-ticipant, and in a subset of 22 participants, we prepared two separate libraries as technical replicates to evaluate

the replicability of our computational methods. This re-sulted in a total of 711 libraries included in this study. Li-braries were quantified by the PicoGreen assay and sequenced on either an Illumina HiSeq 2000 or 2500 plat-form with 25 bp paired-end chemistry at the Broad Insti-tute (Cambridge, MA) or the Massachusetts General Hospital (MGH). Library barcodes were demultiplexed per Illumina’s stated best practices. Reads failing Illumina vendor filters were excluded. Read quality was assessed with FastQC v0.11.2 (http://www.bioinformatics.babraha-m.ac.uk). Reads were aligned to human reference genome assembly GRCh37 (GCA_000001405.11) (http://apr2013. archive.ensembl.org/Homo_sapiens) with BWA-backtrack v0.7.10-r789 [87]. Duplicates were marked with SAM-BLASTER v0.1.1 [88]. All alignment manipulation, includ-ing sortinclud-ing and indexinclud-ing, was performed with sambamba v0.4.6 [89]. Alignment quality was assessed using Picard-Tools v1.115 (http://broadinstitute.github.io/picard/), Sam-tools v1.0, and BamTools v2.2.2 [90, 91]. All libraries were evaluated for sequencing and alignment quality on numerous metrics, including mapped read pairs, per-read and pairwise alignment rate, chimeric pair frac-tion, haploid physical coverage, per-read and pairwise duplicate rate, median insert size, and insert size me-dian absolute deviation (MAD). All libraries except for those generated from the three referred clinical cases with large cytogenetic abnormalities were analyzed genome-wide for the full mutational spectrum of SV, the methods for which are described below.

Case UTR22 was recently described in a separate study [9], but the sequencing protocols used for this case are briefly restated here as follows: a liWGS library was prepared using the Illumina mate-pair library kit. The library was sequenced on an Illumina NextSeq using paired 75 bp reads. The same DNA sample was also se-quenced by paired-end siWGS on an Illumina HiSeq X instrument (paired 151 bp reads). Reads were aligned to the reference genome assembly GRCh37 using BWA-0.7.5a [87]. SV discovery in the UTR22 siWGS library was conducted using Manta with standard settings for siWGS [92] and an independent custom pipeline for liWGS [17].

lrWGS library preparation and sequencing

Prior to 10X Genomics lrWGS library construction, gen-omic DNA samples were checked for fragment size distribution and were quantified. Genomic DNA frag-ment size distributions were determined with a Caliper Lab Chip GX (Perkin Elmer) to quantify DNA above 40 kb in length. Size selection was performed on 1.2 ug of genomic DNA with an 0.75% Agarose cassette on the Blue Pippin platform (Sage Science) with target specifi-cations set to start at 40 kb and end at 80 kb. Samples were quantified using the Quant-it Picogreen assay kit

(Thermo Fisher) on a Qubit 2.0 Fluorometer (Thermo Fisher) and normalized to a starting concentration of 1 ng/uL with TE (0.1 mM EDTA). Starting concentrations of 1 ng/uL were confirmed by picogreen and libraries were subsequently created in accordance with the 10X WGX protocol (10X Genomics). Library size was determined using the DNA 1000 Kit and 2100 BioAnalyzer (Agilent Technologies) and quantified using quantitative PCR (qPCR) (KAPA Library Quantification Kit, Kapa Biosys-tems). The finished WGX libraries were run on an Illumina HiSeqX platform at paired 151 bp reads with an eight-base single index read at the Broad Institute. Upon completion of sequencing, the resulting BCL files were processed by the Long Ranger Pipeline (10X Genomics) for alignment, variant discovery, and phasing.

Structural variation discovery from liWGS

A joint-calling consensus framework, Holmes, was devel-oped for computational SV discovery optimized for liWGS libraries. This pipeline involves the integration of several SV signals simultaneously in batches of liWGS li-braries. The codebase for this pipeline is open-source and publicly available per details listed in“Availability of Data and Materials.” We ran this SV discovery pipeline on sequential batches of 278, 229, and 201 libraries and merged the SV calls from each batch post hoc. For all analyses, only the primary GRCh37v71 assembly was considered and the mitochondrial chromosome was also excluded. Although segments of this pipeline have been described in previous publications [4, 5, 10, 37, 38, 43], each stage is enumerated below.

Anomalous read-pair clustering algorithm

Non-duplicate pairs of primary alignments were first clustered per library with our previously described single-linkage read-pair clustering algorithms BAMStat and ReadPairCluster at a minimum cluster size of three pairs and a minimum clustering distance corresponding to the library’s median insert size plus seven MAD [5, 10, 38]. The clustered read pairs were filtered to exclude pairs in which both reads were multiply mapped (BWA MapQ = 0), pairs where one or both reads mapped to annotated somatic hypermutable sites (antibody parts; “abParts”), and pairs where one or both reads mapped to a set of genomic loci known to cause clustering bias in paired-end WGS data adapted from a list compiled by Layer et al. [93]. The remaining anomalous pairs from the initial per-sample clustering were then pooled across all samples and jointly clustered at a minimum cluster size of three pairs and a minimum clustering distance of the maximum clustering distance used for any individual sample in each processed batch. These joint clusters were heuristically classified with a decision tree algo-rithm that modeled average mapping quality of the

component read pairs, ratio of anomalous pairs in the cluster to proper pairs spanning the same interval as the read-pair cluster, ratio of anomalous pair coverage at the putative breakpoint as compared to the median haploid physical coverage of the library, uniqueness of read map-ping positions, and maximum span of reads on either side of the putative breakpoint. Thresholds for this deci-sion tree were trained on known valid and invalid break-points as determined by previous molecular validation [4, 5]. Each cluster was categorized based on its SV signature: deletion, insertion, inversion, or translocation. These paired-end mapping signatures have been previ-ously described [3, 43, 94]. Hybrid clusters representing two proximal independent variants were separated post hoc via assessment of non-overlapping subgrouping spans between individual samples.

Physical sequencing depth algorithm

In parallel with our cluster-based analysis, we also inves-tigated read depth across our cohort using a version of the cn.MOPS algorithm modified to accommodate liWGS data. This modification begins by dividing the genome into 1 kb bins and counts the number of prop-erly aligned read pairs whose insert spans each bin (i.e. approximate binned physical coverage), rather than counting the raw number of reads per bin, which is the default setting. cn.MOPS was then run on these 1 kb binned values and further run at larger bin sizes of 3 kb, 10 kb, and 30 kb, which correspond to minimum call sizes of 3 kb, 9 kb, 30 kb, and 90 kb, respectively. The resultant CNV segments were merged across all four bin size runs with BEDTools merge to preserve breakpoint resolution while avoiding overly segmented CNV calls [95]. Supplementing the genome-wide read-depth calling provided by cn.MOPS, we developed a statistical machine-learning framework for local copy state geno-typing across all putative CNV intervals based on the same physical depth of coverage matrix used in cn.MOPS CNV discovery. Candidate CNV intervals and their associated sample IDs were input into this geno-typing algorithm and a unidirectional t-test was used to evaluate the significance between normalized physical coverage across samples predicted to harbor the CNV and predicted reference samples. The power and per-muted p value of the t-test were evaluated; we set thresholds of 0.8 and 0.01, respectively, for being suffi-ciently powered and statistically significant to effectively discriminate alterations in copy state between the two groups of libraries (predicted CNV carriers and pre-dicted diploid/reference samples). For singleton CNVs, as well as sites with insufficient power (<0.8), a single sample z-test was used per individual library and re-quired p≤ 1 × 10–6 for a non-reference copy number assignment; this threshold was adjusted to p≤ 1 × 10–4if

the diploid cluster standard deviation was particularly noisy (>0.1). Male and female samples were segregated for all depth-based CNV analyses on allosomes.

Consensus categorization of canonical CNVs

Canonical CNVs (i.e. CNVs with no additional complex-ity beyond deletion or tandem duplication) were catego-rized by a tiered consensus framework to integrate depth-based CNV segments with paired-end clusters (Additional file 2: Figure S8). CNV sites were first nucle-ated on the presence of paired-end clustering support. Next, all cn.MOPS CNV intervals were merged across all samples simultaneously by clustering 5’ and 3’ break-points on proximity independently at a maximum dis-tance of 10 kb per breakpoint between overlapping CNV intervals. The mean breakpoint coordinate was taken when two or more intervals were merged by this ap-proach. These non-redundant cn.MOPS intervals were then overlaid atop paired-end clusters by BEDTools intersect requiring 50% reciprocal overlap and at least one sample shared between both calls, with any cn.MOPS intervals meeting these criteria being merged into the paired-end clusters. In this instance, the union of samples between cn.MOPS and paired-end clustering calls was used and the breakpoint coordinates from the paired-end clusters were retained, since short-read pair-wise mappings have finer breakpoint resolution (generally < 1 kb; improves with increased number of observations) than depth-based CNV segmentation (generally≥ 3 kb) in our approach. When overlap was found between a cn.MOPS interval and a paired-end cluster, the fraction of overlapping samples between these two calls was re-corded. Any cn.MOPS interval that did not match a paired-end cluster was treated as an independent CNV interval for the remainder of the consensus CNV pipeline. At this stage, all putative CNVs were copy-state genotyped in all samples as described above, with CNV genotypes be-ing used to affirm or refute a putative CNV call. Finally, all resultant CNV calls were intersected using BEDTools coverage against a blacklist compiled of annotated dis-persed multicopy loci (e.g. segmental duplications/low-copy repeats), annotated heterochromatin, known sites of systematic short-read mappability biases [93], and gaps in the reference assembly; any CNV covered≥ 30% by size by these intervals was marked as less reliable due to the underlying genomic context (a.k.a. “blacklisted”) [95]. CNVs were assigned a qualitative confidence score (high, medium, or low) based on the above filters (see Additional file 2: Figure S8), and only high-confidence and medium-confidence CNVs were considered for genome-wide ana-lyses. Low-confidence CNVs were recorded and retained for future follow-up studies but were not included in any analyses presented in this manuscript.

Resolving cxSV sites

All candidate instances of cxSVs (i.e. variants involving two or more different distinct SV signatures or three or more breakpoints) were linked if at least one side of two or more paired-end cluster putative breakpoints were separated by no more than the joint clustering distance used in that batch of libraries and involved a cluster shared by at least one sample, or if the clusters were two opposing unmated breakpoints (e.g. a candidate inver-sion junction with only 5’/5’ oriented read pairs and a second candidate inversion junction with only 3’/3’ ori-ented read pairs) whose separating distance either over-lapped with a cn.MOPS CNV segment in at least one shared sample (via BEDTools intersect, reciprocal overlap 50% required) or was otherwise the only parsimonious resolution for both breakpoints after manual scrutiny of both unmated clusters and all discordant individual read mappings near the unresolved breakpoints. All putative complex SV sites were subsequently categorized by a cus-tom shell script. Complex SV subclasses that could be automatically resolved by this process included all combi-nations of CNV-flanked inversions (delINV, INVdel, dupINV, INVdup, delINVdel, dupINVdup, delINVdup, dupINVdel), interspersed duplications (iDUP and iDUP-del), and inverted tandem repeats (IR). All computation-ally predicted complex variants were then manucomputation-ally inspected and revised if necessary. All remaining unre-solved putative complex sites were manually investigated where there was evidence of at least six anomalous read-pairs in support per sample, the event appeared in less than 30% of all libraries, or the event featured overlapping paired-end clustering and read-depth CNV segments. All sites unable to be resolved manually or computationally were emitted from the overall SV pipeline as incompletely resolved sites (IRS).

SV callset curation

All SV calls output by Holmes were subjected to manual inspection to ensure a high-confidence final SV callset. All canonical inversions ≥4 kb, translocational insertions ≥ 4 kb, canonical CNVs≥ 100 kb, chromosomal transloca-tions, and cxSV were evaluated. Manual inspections con-sisted of assessing read pair clusters on mapping quality, plotting read-pair mapping coordinates, and—where applicable—visualizing normalized physical sequencing depth with CNView at predicted sites of increased or de-creased copy number, resulting in visual confirmation of the proposed structure in >95% of manually inspected ob-servations [96]. Second, since all liWGS libraries were pre-pared from lymphoblastoid cell line (LCL)-derived DNA, we screened our SV callset for large LCL passaging arti-facts. We required all unbalanced SVs≥ 100 kb with less than 30% coverage by size of our CNV blacklisted regions (see above) that appeared in 1/686 participants to have at

least one source of orthogonal validation performed on whole blood-derived DNA (most commonly CMA; see section on SV breakpoint validation, below), resulting in an estimated 26 LCL artifacts that were not present in the blood DNA. We also excluded any balanced rearrange-ments validated in LCL-derived DNA but not in whole blood-derived DNA due to likely being LCL passaging artifacts (n = 2). It is likely that a comparable subset of smaller SVs observed in this study (< 100 kb) may also be LCL artifacts; however, given the high concordance of the callset when compared to two independent sources of val-idation from whole blood-derived DNA (see “SV break-point validation” below), we do not anticipate remaining LCL artifacts to be numerous.

Callset merging across sequencing batches

SV callsets from each batch of liWGS libraries (referred to hereafter as “set 1” (n = 278), “set 2” (n = 229), and “set 3” (n = 201), respectively) were merged using an it-erative approach as follows. First, a list of non-redundant SV breakpoints was linked between sets. Breakpoints were linked if their mapping spans had at least 20% overlap between sets and their predicted SV type was concordant. Where multiple breakpoint clus-ters were putatively linked from within the same set, clusters were preferentially selected if they were classi-fied as“Valid” by our heuristic classifier (see above), then ranked by differences in variant allele frequency from the original breakpoint, selecting the top match among this list from each set. Each breakpoint from each set was only allowed to correspond to one non-redundant merged breakpoint, and each non-redundant merged breakpoint could contain at most one breakpoint from each set. The union of samples represented by all linked clusters was taken to create the consolidated list of unique subjects represented in each non-redundant breakpoint cluster. We scrutinized the outcome of this breakpoint linking procedure and identified only 2 total sites (0.01% of all SVs; 1 cxSV and 1 INS) where two similar SVs were not merged into a single consensus variant based on proximal breakpoint coor-dinates (Collins2017_INS_459 & Collins2017_INS_460; Collins2017_cxSV_213 & Collins2017_cxSV_214; see Additional file 1). Next, any canonical CNV segments not linked based on read-pair clustering as described were further considered for linking between sets based on re-ciprocal overlap≥ 50% by size with another canonical CNV segment from a different set. Where multiple canon-ical CNV segments were eligible for linking from a single set, the CNV with the greatest reciprocal overlap with the original segment was selected. CNV confidence was reas-signed to the merged non-redundant CNV segments based on the highest confidence of any contributing CNV. For all analyses, we excluded canonical CNVs designated

as low-confidence (n = 6660; not included in any counts reported in “Results,” “Discussion,” figures, tables, or supplement).

SV validation experiments

We employed five approaches for validation of SVs de-tected in this cohort, as detailed below.

PCR cloning and sanger sequencing

SV validation was performed on 144 SVs with traditional PCR cloning and Sanger sequencing. Primers for break-point cloning and Sanger sequencing were designed with Primer3 run at default parameters [97]. Candidate primers were further screened for degenerate hybridization and non-specific product via BLAT and in silico PCR [98]. Primers were synthesized by Integrated DNA Technolo-gies Inc. (Coralville, IA, USA). PCR products were visual-ized by gel electrophoresis. Sanger sequencing was conducted by GeneWhiz Inc. (South Plainfield, NJ, USA) and the MGH DNA Core (Boston, MA, USA). Sequence alignment was resolved using UCSC BLAT [98]. PCR and Sanger resequencing was performed for a subset of break-points from cases TL009, TL010, and UTR22, but these validation experiments were not included for any perfor-mances estimates per the genome-wide SV analyses.

CMA analysis

CNV detection from SNP CMA was previously performed on 99.0% (679/686) of sequenced subjects used in genome-wide SV analyses, which has been previously described in detail [36, 99]. In brief, genotyping was per-formed with the Illumina Omni2.5, 1Mv3, or 1Mv1 arrays. CNVs were detected with the CNVision algorithm, which calculates a joint probability for a variant based on three methods (PennCNV, QuantiSNPv2.3, and GNOSIS) [36, 100, 101]. For the purpose of our analysis, we selected un-balanced SVs most likely to be detected at CMA reso-lution and thus restricted to the 1170 autosomal SVs with at least one segment of predicted dosage imbalance≥ 40 kb that also did not have≥ 30% coverage by size with regions of known dosage biases or low-complexity se-quences included in our blacklist used during CNV detec-tion, as described earlier. We assessed overlap between CMA-based CNV segments and our predicted intervals of dosage imbalance from liWGS using BEDTools requir-ing≥ 50% coverage by size from CMA CNV calls over the predicted liWGS CNV interval [95]. We considered any SVs with at least one segment of dosage imbalance consid-ered in this analysis that validated in at least one expected sample to represent a true positive SV call.

Capture sequencing and analysis

Multiplexed high-throughput validation was conducted by simultaneous breakpoint capture sequencing of 427

predicted SV sites across 96 child–parent trios (288 indi-viduals). Breakpoints were selected to represent all pos-sible SV classes; priority was given to rare variants, those predicted to disrupt genes of interest, and those that did not already have orthogonal validation from CMA ana-lysis or PCR and Sanger sequencing at the time of the capture validation experiment. Targeted capture probes were tiled across 2250 bp, flanking both sides of each breakpoint; probe density was progressively concen-trated nearest the expected position of the breakpoint to maximize sequencing depth crossing and directly flank-ing predicted breakpoints. Degenerate probe sequences (i.e. probes with multiple possible hybridization sites in the reference genome) were identified by a combination of the Jellyfish k-mer counting algorithm and in silico probe sequence alignment with BWA-mem; all degen-erate probes were removed from the capture design [102, 103]. Library capture enrichment was performed using the Agilent (Santa Clara, CA, USA) SureSelect XT system and protocols. Ninety-six pools of three samples were prepared, where each pool contained the DNA from one participant, an unrelated mother, and an unrelated father, where all three individuals in the pool were not predicted to share any breakpoints present in the capture design. These 96 pools were barcoded, multiplexed, and sequenced once with a full lane of single-end 101 bp reads and once with a full lane of paired-end 101 bp on an Illumina HiSeq 2500 at the Broad Institute (Cambridge, MA, USA). Two sets of 12 pools received additional sequencing at single-end 150 bp and single-end 300 bp on the Illu-mina MiSeq platform at MGH to test the effect of longer read lengths in this capture design. Sequencing data were processed as described previously for liWGS libraries. Across all 96 capture libraries, a total of 6.23 billion reads were generated. Sequences cross-ing putative SV breakpoints (and thus overall SV val-idity) were obtained by blindly screening all capture data for high-quality individual non-duplicate reads with a primary alignment flanking one side of the predicted breakpoint and a secondary or supplemen-tary alignment flanking the other side of the predicted breakpoint. All candidate split-read sequences were evaluated manually using BLAT to ensure they did not have any equally parsimonious alignments any-where else in the genome [98]. A subset of break-points showed paired-end clustering support without a split read, which we included if they showed a sta-tistically significant enrichment of paired-end reads relative to predicted reference samples.

liWGS versus siWGS overlap

We evaluated the overlap between SV calls from the 39 participants for which previously generated siWGS data

were available [104]. We considered two approaches for validating liWGS SV calls from siWGS data. For all com-pletely resolved liWGS SV calls (i.e. excluding IRS) appear-ing in at least one of the 39 participants with near-breakpoint precision (i.e. any call with at least one cluster of anomalous liWGS read pairs; n = 2399), we searched that participants’ corresponding siWGS library within a window of ±5 kb from the liWGS-predicted breakpoint coordinates for any anomalous, non-duplicate, primary aligned siWGS pairs mapping to within the 5 kb windows of the predicted breakpoint. Further, we required the aligned orientation of siWGS pairs to match those of the corresponding liWGS pairs. Windows of 5 kb were chosen as the upper bound of conceivable breakpoint imprecision from liWGS alone. Any SV with one breakpoint supported by≥ 3 unique siWGS read pairs meeting our criteria in at least one expected sample was considered a true positive liWGS call. When comparing siWGS data against our pre-dicted“invalid” clusters of anomalous liWGS read pairs to estimate false negative rates, we conservatively relaxed these thresholds to ±7.5 kb and≥ 1 unique siWGS read pair. Second, we evaluated evidence from siWGS sequen-cing depth for all completely resolved (i.e. excluding IRS) autosomal liWGS SV calls appearing in at least one of the 39 participants with at least one interval of dosage imbal-ance≥ 10 kb that had < 30% coverage by our blacklisted CNV loci (n = 585; 514 of which also were considered dur-ing siWGS read-pair analysis). For this analysis, we first ran cn.MOPS on siWGS libraries for all 39 participants and their families (mothers, fathers, and one sibling each) from available data [104, 105]. Similar to our application of cn.MOPS during liWGS SV discovery (see above), we ran cn.MOPS on this siWGS dataset at bin sizes of 100 bp, 300 bp, 1 kb, and 3 kb, resulting in minimum CNV call sizes of 300 bp, 900 bp, 3 kb, and 9 kb, respectively. We merged the resultant calls per sample across these three bin sizes to obtain an initial set of depth-based CNV calls for comparison versus liWGS. For each interval of dosage imbalance from liWGS that met our criteria for this ana-lysis, we evaluated coverage of that interval against siWGS cn.MOPS calls from that same participant. Any liWGS call with an interval of≥ 50% coverage by siWGS cn.MOPS calls in at least one expected sample was considered a true positive liWGS SV call. The total number of non-redundant SVs considered by either read-pair or sequen-cing depth analyses versus siWGS was 2470.

liWGS sensitivity analysis versus CMA CNVs

We evaluated the sensitivity of liWGS for detection of high-confidence CNVs reported by CMA. As the reso-lution of CMA is variable across the genome (for ex-ample, based on the probe density at a given locus), we applied filters to the raw CNV calls from CMA on the

subset of 99.0% of participants in this study for which CMA CNVs had previously been reported [36, 99]. We thus required CMA CNV calls to be≥ 25 kb, have < 30% coverage by size versus the CNV blacklist applied during liWGS SV discovery, and have a pCNV≤ 1 × 10–9 as required by the published methods for CMA CNV analyses in these same participants by Sanders et al. [36, 99]. For each CMA CNV meeting these criteria, we compared the CNV interval to the predicted intervals of dosage imbalance from fully resolved liWGS SV calls (including canonical CNVs and also unbalanced cxSVs). We considered a CMA CNV to be successfully detected by liWGS if the CMA CNV interval had≥ 25% coverage by size from regions of dosage imbalance from that partic-ipant’s corresponding liWGS SVs. We did not observe major differences in the outcome when requiring different stringencies of reciprocal overlap (up to ~75%).

liWGS technical replicate analysis

For 22 participants, we sequenced pairs of technical replicate liWGS libraries to assess the consistency of our SV discovery methods, as described above. Given that pairs of technical replicates varied in coverage, and since depth of coverage can bias sensitivity in many variant detection applications [106], we desig-nated the replicate with fewer total fully resolved SV calls in each pair as the truth library and the second replicate as the test library. For each pair, we evalu-ated concordance of SV calls as the total number of fully resolved SVs from the truth library detected in the test library divided by the total number of fully resolved SVs in the truth library.

Comparison to other studies and SV reference databases

We downloaded SV callsets as reported in six recent WGS studies of SV outside the SSC [1, 5, 7, 46–48] and two public SV reference databases [49, 50]. We next decomposed each callset into sets of genomic intervals representing deletion, duplication, inversion, and inser-tion. For studies where cxSVs were reported as multiple intervals (e.g. a delINVdel reported as two deletion in-tervals and one inversion interval), we separated those intervals into their respective categories prior to com-parisons. For studies where cxSVs were reported only as one single interval with no additional information, we treated that interval as a composite complex interval for sake of comparisons. For classes of SV reported that did not fit into any of these previous categories, we added them to a final “other” SV category. From these cleaned callsets, we compared each of the SVs identified in this study to its respective SV category as well as the “other” SV category. For cxSVs, we compared each rearranged interval identified in our study to its respective category and also compared the entire interval spanned by the

cxSV to the complex and “other” categories. We deter-mined two intervals to be concordant if they shared 50% reciprocal overlap by size per BEDTools intersect. cxSVs were considered successfully matched in their entirety if all intervals involved in the rearrangement as identified by liWGS in this study had a matching interval in the comparison datasets. If one or more intervals involved in a cxSV were not matched in any of the reference datasets, we considered that cxSV to have been previ-ously discovered but incompletely characterized.

Evaluating the relationship between inversion breakpoints and long repetitive sequences

We first annotated all inverted loci involved in complex and canonical SVs excluding insertions against annotated repetitive sequences at least 300 bp in length from RepeatMasker and the UCSC segmental duplication track for human assembly GRCh37 [61, 107]. As liWGS does not provide nucleotide-level precision of break-points, and instead usually offers a breakpoint resolution of ~1.5 kb, we drew a conservative window of ±500 bp around each predicted inversion breakpoint and inter-sected against the set of repetitive elements described above using BEDTools intersect while requiring at least one base of overlap [95]. We next shuffled all inversion intervals across the GRCh37 reference genome with BEDTools shuffle, and did not allow breakpoints to be placed in N-masked reference sequences to avoid artifi-cially depleting our simulated inversions from mappable regions of the genome. Importantly, for each simulated set of inversions, we maintained the original size distri-bution of inversions derived from the experimental liWGS data. We next repeated the repetitive sequence annotation process for each set of simulated inversions, and calculated empirical p values by comparing our ob-served values against all simulated values. We calculated p values for all repeat elements in aggregate, but also considered the four most common repeat families inde-pendently: SINEs, LINEs, LTRs, and segmental duplica-tions (Seg. Dup.). Finally, we adjusted p values for multiple comparisons using a Benjamini–Hochberg correction.

Genome-wide SV enrichment tests

To assess our callset for the presence of loci enriched in SV beyond random chance, we first segmented the GRCh37 reference genome into 100 kb contiguous bins. We next removed all bins that had at least 10% covered by the CNV mask applied during SV detection to avoid observing artificially depleted bins due to technical limi-tations. We further restricted this analysis to autosomes. We then overlaid all SVs discovered in this cohort atop the remaining bins (n = 24,742) and counted the number of SVs per bin. We tabulated counts per bin for all fully

resolved SVs (i.e. excluding IRS) as well as counts spe-cific to each major SV class except IRS (DEL, DUP, INS, INV, CTX, cxSV). We next made the null assumptions that large SVs are (1) rare events in the genome (as com-pared to SNPs or InDels) and (2) that they should follow a random distribution across the genome. Given that these assumptions fit the description of a Poisson point process, similar to the observation of sequencing reads by Lander and Waterman [108], we thus evaluated a Poisson test (λ = mean count of SVs per bin) for the count of SVs per bin to evaluate the alternative hypoth-esis of enrichment of SVs at the tested loci beyond ex-pectation (e.g. hypermutable or repeatedly rearranged loci). We subsequently applied the Benjamini–Hochberg procedure to control FDR and assessed genome-wide significance at q≤ 0.05. Finally, where multiple 100 kb bins each emerged as significantly enriched for SVs be-yond expectation and were not separated by more than a single non-significant 100 kb bin, we merged those bins into one larger locus and assigned the maximum p value of any one sub-bin to the larger locus.

Gene annotation

All completely resolved SVs (i.e. excluding IRS) were evaluated for possible genic overlap by breakpoint com-parison with all annotated transcripts from the Ensembl gene annotation GTF for hg19/GRCh37 [109]. Intersec-tions were performed with BEDTools intersect for single-breakpoint variants and BEDTools pairtobed for mutli-breakpoint variants [95]. Deletions were classified as LoF if they altered at least one base from any anno-tated exon. Duplications were classified as LoF if they duplicated one or more bases from any annotated in-ternal exon (i.e. neither the 5’ UTR, 3’ UTR, first exon, or last exon) without spanning beyond the first or last exon of the gene and were classified as whole-gene copy gain (CG) if the duplication encapsulated an entire an-notated transcript. Inversions were classified as LoF if one breakpoint localized to an annotated transcript and the other breakpoint localized outside that transcript or if both breakpoints lay within the same transcript and the interval between the two breakpoints spanned at least one annotated exon. Translocations were consid-ered LoF if either breakpoint lay within an annotated transcript. Given that the resolution of liWGS did not permit exact breakpoint base-pair-scale mapping, we did not consider insertions for LoF or CG gene impacts, but did make note if inserted sequence originated from a gene or if sequence was being inserted into a gene. Complex events were annotated by first decomposing the variant into its constituent SV signatures, then interpreting each SV signature simultaneously with the methodology de-scribed above to reach a consensus on the overall genic impact of the rearrangement. All interpretation of genic